Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks.

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Artificial Intelligence in Medicine

Background:

• Normal Pressure Hydrocephalus (NPH) is a neurological disorder characterized by enlarged ventricles and dementia-like symptoms, potentially reversible with surgery.
• Accurate segmentation of the ventricular system in MRI is vital for characterizing NPH but challenging for existing algorithms, especially with enlarged ventricles.
• Deep convolutional neural networks (CNNs) show promise for fast and accurate medical image segmentation.

Purpose of the Study:

• To develop and evaluate a 3D U-net CNN-based network for segmenting the human ventricular system in MRI scans.
• To compare the performance of networks trained on different datasets, including healthy controls (HC) and NPH patients.
• To assess the efficacy of the proposed method against state-of-the-art segmentation techniques.

Main Methods:

• Implementation of a 3D U-net convolutional neural network architecture for volumetric segmentation.
• Training and evaluation of three distinct networks using magnetic resonance imaging (MRI) datasets from HC and NPH patients.
• Comparative analysis of segmentation performance across different training strategies and against existing methods.

Main Results:

• Networks trained solely on HC data demonstrated poor performance in segmenting NPH patient ventricles, even those appearing normal.
• The 3D U-net network trained on a combined dataset of HC and NPH images achieved superior segmentation accuracy.
• The proposed CNN-based approach outperformed current state-of-the-art methods on both HC and NPH datasets.

Conclusions:

• A 3D U-net CNN model trained on diverse datasets (HC and NPH) is essential for robust ventricular segmentation in NPH.
• Exclusively training on healthy control data leads to significant performance degradation in NPH patients.
• This deep learning approach offers a promising tool for improved characterization and potential treatment guidance in Normal Pressure Hydrocephalus.